Recipient Organization
CORNELL UNIVERSITY
(N/A)
ITHACA,NY 14853
Performing Department
(N/A)
Non Technical Summary
US and global agriculture face challenges in sustainability, productivity, profitability, environmental integrity, resilience, rural prosperity, and inclusiveness. Data-driven technologies that might address these challenges are proliferating, but in a haphazard and disconnected manner. The massive streams of data and frequent reports or alerts generated by today's agricultural data systems, all running unique software and producing data in disparate formats, overwhelm farm personnel. Data increase in variety and amount faster than models can be developed to analyze them or networks can be built to carry them. Few technologies are well tested, making their selection and incorporation into the farm suite difficult. The Farm of the Future (FotF) requires research-based insights to resolve these and related problems.The Cornell Agricultural Systems Testbed and Demonstration Site (CAST) for the FotFwill harnessdata-driven technology for a sustainable and resilient US agricultural system. CAST will consistof a networked cluster of test farms with associated facilities and personnel that will leverage the resources of Cornell University and its partners to conduct data-driven research, extension, and education under the aegis of the Cornell Institute for Digital Agriculture (CIDA).CAST will advance, evaluate, and demonstrate data-driven solutions for food systems. Amultidisciplinary team of researchers, extension specialists, and educators from Cornell University (CU) and the University of Arkansas at Pine Bluff (UAPB) will undertake a comprehensive, systems-based approach to research, extension, and education, focusing on specific field-crop and animal models to generate knowledge, experiences, and opportunities with application to these agricultural sectors. CAST will leverage existing knowledge, resources, and cross-disciplinary activities through multiway collaboration withprivate and public stakeholders in food systems.CAST will promote stakeholder engagement in a commercial-farm-like setting where technologies and practices can be tested, their data collected, integrated, and analyzed, and their effects on decisions, animals, the environment, and people discerned. CAST will also be central to extension and education, on-site and virtually: farmers, students, researchers, and other stakeholders will help shape its research agenda, and knowledge produced will be fed back to all through continuous extension and education.CAST research aims will focus ondemonstratingthe value of integrating existing and emerging data-driven technologies and practices under commercial-farm-like conditions. Research will be organized infour areas (1) Innovation in Technology and Farm Practices, (2) Data Integration, (3) Data Analytics and Decision Support, and (4) Impact Assessment.At CAST scientific groundwork for innovation, demonstration, and evaluation of data-driven technology and management practices for farming will be conducted. CAST's unique ecosystem will support integration and testing of commercially available technologies and development, deployment, and testing of technologies in the research pipeline. The economic, environmental, and social outcomes of adopting the proposed technology solutions will be quantified using a combination of economic analysis, systems modeling, and behavioral research. CAST will enhance and demonstrate the value of integrating a wide range of existing and emerging technologies and practices.Extension activities will promote exchange of knowledge between CAST and stakeholders for harnessing technology to build more sustainable, resilient, and equitable farms and communities.The stakeholder network to be developed for this project--the CAST Network for Extension and Teaching (CAST-NET)--will involve farmers, manufacturers, consultants, academic experts, and others in every stage of problem identification, planning, implementation, evaluation, and feedback. CAST-NET will provide insights about cutting-edge technology goals, actively support adoption, and build informed trust that forthcoming technologies will repay the cost and effort required to adopt them. To promote adoption of innovations developed and demonstrated at CAST, we will communicate our vision, activities, and actionable outputs to CAST-NET and other stakeholders by providing access to in-person and virtual demonstrations, testing, evaluations, and new knowledge.Education efforts at CAST willprovide real-world, hands-on educational experiences to the next generation of agricultural leaders, scientists, and professionals.Benefits offered by new technologies will be sustained by the next generation of engaged, enthusiastic, and well-prepared students. CAST's cluster of working farms, where purposeful experiences range from handling actual soils, plants, and animals to coding, device testing, and hypothesis testing, will provide rich opportunities for experiential learning. Students will create, experiment, and experience in the development, delivery, and evaluation of technologies for the FotF. We will leverage existing programs and create new educational initiatives at CU and UAPB that employ the resources of CAST. These will include a minor and coursework in digital agriculture, internships at CAST, and a student hackathon. Through these efforts students will be engaged in research, outreach, and science communication.This projectwillfulfillthe FotF program's vision of a rural testbed supporting research, extension, and education in precision agriculture, smart automation, and data connectivity. Cornell university, the University of Arkansas at Pine Bluff, and their partners will establish the CAST testbed and realize its potential for advancing climate-change mitigation, environmental health, material and economic sustainability, and the well-being of rural communities.CAST will demonstrate efficient use of resources, reduced environmental impact, and the human role in agriculture. Improved efficiencywill increase productivity while reducing costs and environmental impacts. For example, precision agriculture will matchinputs and interventions to crop requirements in time, space, type, and quantity to optimize crop genetics and productivity.Animal agriculture will achieve efficiencies through data-driven approaches that individualize feeding and handling, identify sick animals promptly, guide precision fertility interventions, and the like.Reducing agriculture's environmental impacts is imperative to mitigating climate change, ocean dead zones, soil loss, water and air pollution, insect decline, zoonosis, and other harms. Data-driven technologies and practices of conservation, regeneration, and circular economy can reduce, eliminate, or even reverse undesired effects of food production. As farms get larger with fewer people working on them, but at higher wages, data-driven technologies are increasingly used to replace low-wage labor. These technologies can improve the remaining workers' quality of life by reducing the most burdensome tasks, eliminating others, and supporting efficient decision-making. We will determinesocial, socioeconomic, and farm-level financial impacts of technology adoption and integration.CAST's long-term goal is a truly sustainable FotF: a carbon-neutral or -negative, biodiversity-enhancing, inclusive, humane farm that restores rather than consumes the basis of its own existence.
Animal Health Component
40%
Research Effort Categories
Basic
30%
Applied
40%
Developmental
30%
Knowledge Area
601 - Economics of Agricultural Production and Farm Management;
102 - Soil, Plant, Water, Nutrient Relationships;
803 - Sociological and Technological Change Affecting Individuals, Families, and Communities;
205 - Plant Management Systems;
404 - Instrumentation and Control Systems;
307 - Animal Management Systems;
402 - Engineering Systems and Equipment;
Subject Of Investigation
3499 - Dairy cattle, general/other;
1699 - Pasture and forage crops, general/other;
6099 - People and communities, general/other;
3410 - Dairy cattle, live animal;
7410 - General technology;
7210 - Remote sensing equipment and technology;
2499 - Plant research, general;
Field Of Science
2090 - Statistics, econometrics, and biometrics;
3010 - Economics;
3100 - Management;
Goals / Objectives
We propose the Cornell Agricultural Systems Testbed and Demonstration Site (CAST), a networked cluster of test farms with associated facilities and personnel that will leverage the resources of Cornell and its partners to conduct data-driven research, extension, and education under the aegis of the Cornell Institute for Digital Agriculture (CIDA).CAST will advance, evaluate, and demonstrate data-driven solutions for food systems. Amultidisciplinary team of new and well-established researchers, extension specialists, and educators from Cornell University (CU) and the University of Arkansas at Pine Bluff (UAPB) will undertake a comprehensive, systems-based approach to research, extension, and education, focusing on specific field-crop and animal models to generate knowledge, experiences, and opportunities with application to these agricultural sectors and far beyond. Under the framework of CIDA and other Cornell initiatives, we will leverage existing knowledge, resources, and cross-disciplinary activities through multiway collaboration; private and public stakeholders in food systems will be actively engaged. CAST will promote stakeholder engagement in a commercial-farm-like setting where technologies and practices can be tested, their data collected, integrated, and analyzed, and their effects on decisions, animals, the environment, and people discerned. CAST will also be central to extension and education, on-site and virtually: farmers, students, researchers, and other stakeholders will help shape its research agenda, and knowledge produced will be fed back to all through continuous extension and education. Our Aims are:AIM 1: Research. At CAST we will conduct scientific groundwork for innovation, demonstration, and evaluation of data-driven technology and management practices for farming. CAST's unique ecosystem will support integration and testing of commercially available technologies and development, deployment, and testing of technologies in the research pipeline. The economic, environmental, and social outcomes of adopting the proposed technology solutions will be quantified using a combination of economic analysis, systems modeling, and behavioral research. CAST will enhance and demonstrate the value of integrating a wide range of existing and emerging technologies and practices. Our specific objectiveisdemonstratingthe value of integrating existing and emerging data-driven technologies and practices under commercial-farm-like conditions. To accomplish this goal research will be organized under four thrust areas:(1) Innovation in Technology and Farm Practices, (2) Data Integration, (3) Data Analytics and Decision Support, and (4) Impact Assessment.AIM 2: Extension. The stakeholder network to be developed for this project--the CAST Network for Extension and Teaching (CAST-NET)--will involve farmers, manufacturers, consultants, academic experts, and others in every stage of problem identification, planning, implementation, evaluation, and feedback. CAST-NET will provide insights about cutting-edge technology goals, actively support adoption, and build informed trust that forthcoming technologies will repay the cost and effort required to adopt them. To promote adoption of innovations developed and demonstrated at CAST, we will communicate our vision, activities, and actionable outputs to CAST-NET and other stakeholders by providing access to in-person and virtual demonstrations, testing, evaluations, and new knowledge. Exchange knowledge between CAST and stakeholders will harnesstechnology to build more sustainable, resilient, and equitable farms and communities.AIM 3: Education. Benefits offered by new technologies will be sustained by the next generation of engaged, enthusiastic, and well-prepared students. CAST's cluster of working farms, where purposeful experiences range from handling actual soils, plants, and animals to coding, device testing, and hypothesis testing, will provide rich opportunities for experiential learning. Students will create, experiment, and experience in the development, delivery, and evaluation of technologies for the FotF. We will leverage existing programs and create new educational initiatives at Cornell Universityand the University of Arkansas at Pine Bluff that employ the resources of CAST. Students will be engaged in research, outreach, and science communication. Collectively, theseactivities will provide real-world, hands-on educational experiences to the next generation of agricultural leaders, scientists, and professionals.
Project Methods
CAST will include the state-of-the-art Cornell University Ruminant Center, Cornell Teaching Dairy Barn, and Musgrave Research Farm, which comprise a large (~2,550 acres), diverse land base in NY State. Through systematic integration of data, coordinated technology testing and demonstration, and exchanges of physical materials, these rural farm units will form an advanced hub for research, extension, and education that helps the FotF to fulfill its promise. CAST will focus on field crops and dairy production as models of the US ag economy, these being among the largest sectors in volume and value and offering some of the greatest challenges and opportunities for mitigating climate change. CAST's multi-site nature is a major strength: more can be learned about technologies, practices, and intelligent systems if they are applied across operations of varying size, type, and management. CAST's two crop production units (~2,550 acres available) and two dairy herds (~825 adult cows, 500 youngstock) will generate enough data to realistically model key challenges of integration and analytics.RESEARCH. CAST's four research thrusts will support innovation through cycles of development, deployment, and evaluation of technological and data-driven breakthroughs and test and demonstrate existing and emerging technologies and practices under commercial-farm-like conditions. Research on innovation in technology and farm practices will develop, deploy, test, and demonstrate innovative technologies and management practices under working farm conditions.Under research thrust 1,Innovation in Technology and Farm Practices, Specific Objectives forTechnology-Enhanced Field Crop Productionwill develop, refine, test, and demonstrate technologies and management practices in (1) Precision management of crop inputs, (2)Cover cropping and (3) Soil amendments such as rock dust and biochar.Under Specific ObjectiveSmart Automation and Data-Driven Precision Animal Managementthis project willevolvea suite of technologies in support of precision management and automation in animal systems that can enhance animal and human health, well-being, and performance while improving farm profitability and sustainability. Specifically, research will develop and demonstrate data-driven technologies for (1) Precision feeding and nutritional managementby data-driven ration formulation and automated monitoring and management, (2) Precision health management throughdata-analytic tools using integrated sensor and non-sensor data to predict automatically and in real time health outcomes of cattle, and (3) Precision reproductive monitoring and managementthroughautomated estrous detection tools, data-driven decision support methods using integrated data,automated fertility control,and point-of-care diagnostic devices.Under research thrust 2, Data Integration,the Cornell-developed Software Defined Farm, will convert raw, uninformative data from multiple sources and in diverse formats into high-quality data streams that enable efficient, accurate data analytics, and reporting. Data generated by thehardware and software infrastructure at CAST will be used to develop fully automated processes for real-time data capture and initial processing at the source, standardization of data and metadata, transfer to intermediate and centralized data streams, automated identification of technical failures, and automated discrimination of technical failures versus biological variation.Under research thrust3,Data Analytics and Decision Support,the wealth of data generated and captured by technologies and intensive management practices at the CAST will be interpreted by machine learning algorithms (MLAs) and data analytics to be maximally useful to farmers. We will create, refine, and test several algorithms for improved decision making for soils and crops and for animals at CAST. For cattle outcomes algorithms willautomatically predict health and reproductive outcomes in real time. For field crops outcomes, we will develop AI tools for site-specific management for adjusting inputs rate on a per-zone basis and understand the main drivers of productivity for each zone.Adata integration and fusion infrastructure integrating historical and real-time sensor and non-sensor data will enable the exploration of multiple interactions between the numerous sources of variation driving outcomes of interest.Under research thrust 4,Farm, Food, and Social Systems Impact Assessment, we will build on the knowledge gained to evaluate the expected economic, animal health, environmental, and social/socioeconomic outcomes of adoption of novel data-driven and farm management practices. Specific Objectives include evaluation of (1)Farm Financial Feasibility, (2) Whole Farm Animal and Environmental Health, and (3) Social and Socioeconomic Impacts.EXTENSION.By means of an actively involved stakeholder network, knowledge transfer, and education and training, CAST will promote development, marketization, acceptance, and adoption of the technologies and methods that it develops, tests, and demonstrates. Under Specific Objective 1, we will develop the CAST Network for Extension and Teaching (CAST-NET)in which stakeholders will participate in technology development, refinement, evaluation, and demonstration.Under Specific Objective 2,Knowledge Transfer,extension activities will target knowledge transfer and communication beyond CAST-NET. TheCAST vision and activities will be communicated through virtual platforms, on-site activities, and contributions to existing PRO-Dairy and Cornell Cooperative Extension programming. A website willaggregate and link to virtual content including live and recorded video streamed that will provide virtual CAST demonstrations for stakeholders. Two virtual courses for precision crop and animal management will be built on knowledge generated at CAST.Under Specific Objective 3,Demonstration and Training, industry engagement will include access to demonstrations, hands-on testing, and evaluations for stakeholders interested in technology and management practices employed at CAST. Industry organizations thatpartner with CAST will have access to arich data collection and integration platform for testing, awhole farm impact assessment framework for evaluation of financial and environmental feasibility, and a communications and outreach program for demonstration, on-site training, and visibility.EDUCATION. Educational efforts will focus on experiential learning--creating, touching, doing, interacting, evaluating, and reflecting, with CAST as a living classroom. Under Specific Objective1,a minor in digital agriculture (DA) will be developed to help undergraduates gain new perspectives, network across the university, do research pertinent to DA, and engage with CAST and CIDA.UnderSpecific Objective 2, aVirtual Course in DA for URM Studentswill disseminate knowledge from CAST and provide URM students from outside Cornell with an opportunity to learn from cutting-edge activities in real time.Cornell and the University of Arkansas at Pine Bluff (UAPB) will collaborate on developing a DA introductory course to a virtual format.UnderSpecific Objective 3, we will develop aResearch and Extension Summer Internship Program at CAST.Thisinternship program complements classroom learning with opportunities for extended, hands-on engagement in the development and evaluation of data-driven technologies and management practices at CAST.UnderSpecific Objective 4,Engage CAST in the CIDA Hackathon, CAST will belinkedto the annual CIDA Hackathon which gathers students, faculty, and community members to develop innovative tools and analyses driving DA research.